T. Brian Jones is co-founder and CTO of Inventory Hero. He leads the engineering behind its Amazon data pipeline, demand forecasting, and the AI platform that lets sellers talk to their live inventory, sales, and supplier data in plain language.
Generative engine optimization (GEO) is the practice of structuring and writing content so that AI answer engines (like ChatGPT, Perplexity, and Google AI Overviews) cite, quote, or recommend it inside their synthesized answers. It shifts the goal of optimization away from ranking positions and clicks toward being included and cited in the answer itself. GEO overlaps heavily with SEO; the distinct part is making your content easy for a model to extract and trust, using clear structure, evidence, and authoritative signals.
Does GEO actually work, or is it hype?
Both. The one peer-reviewed study (the KDD 2024 GEO paper) found that adding relevant quotations, statistics, and authoritative citations measurably increased a source's visibility in generative answers, while keyword stuffing actually reduced it. But most GEO advice beyond that is repackaged SEO, and independent audits suggest roughly 70 to 80 percent of GEO is disciplined SEO you should already be doing. It works as a direction, but be skeptical of vendors quoting precise lift percentages as guarantees; citations are non-deterministic and vary by engine.
How is GEO different from SEO?
SEO optimizes for ranking in a list of links; GEO optimizes for being cited inside a synthesized AI answer where there may be no list to rank in. The foundations overlap almost entirely (quality content, clear structure, authority, crawlability), but GEO emphasizes extractability (short, self-contained, quotable passages), evidence adjacent to claims (statistics and citations), and clear entity language so a model can attribute a fact to you. You still do SEO; GEO is a formatting and evidence layer on top.
How do you measure GEO success?
Because there are no rankings to track, GEO is measured by presence in AI answers: share of voice (the percentage of relevant AI answers that mention your brand), citation and mention tracking across engines, and sentiment. Tools like Profound, Peec, and Otterly sample AI answers to estimate these, and you can filter referral traffic from chatgpt.com, perplexity.ai, and similar in GA4. All of these are sampled and probabilistic rather than exact, so track trends over time rather than single snapshots.
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Generative engine optimization (GEO) is optimizing your content so AI answer engines like ChatGPT, Perplexity, and Google AI Overviews cite and recommend it inside their answers. The short version: success is measured by inclusion and citation rather than clicks, the one rigorous study found quotations, statistics, and authoritative citations help most while keyword stuffing hurts, and honestly, most of GEO is disciplined SEO with an evidence-and-extractability layer on top. Below is what the research shows, the tactics that follow, and how to measure it.
For two decades, optimization meant ranking in a list of blue links. AI answer engines change the shape of the result: instead of ten links, a shopper or researcher gets one synthesized answer that may cite a handful of sources. GEO is the discipline of being one of those cited sources.
That reframes the goal. You are no longer only trying to rank; you are trying to be included and quoted in the answer, and ideally recommended by name. The mechanics of how engines choose sources differ (more on that below), but the common thread is that they favor content they can confidently extract and attribute.
Most GEO advice is marketing. The one load-bearing, peer-reviewed anchor is the "GEO: Generative Engine Optimization" paper (Aggarwal et al., KDD 2024), which built a benchmark of roughly 10,000 queries across nine domains and tested which content changes increased a source's visibility in generated answers. The findings that matter:
Adding quotations raised visibility the most in their tests, followed by adding relevant statistics and citing sources.
Improving fluency and adding authoritative language also helped.
Keyword stuffing hurt, reducing visibility. The old SEO trick actively backfires.
Two honest caveats. First, the study's winning tactics (statistics and quotations) allowed fabricated data in the experiment, so the effect may not replicate as strongly when you use only real, verifiable facts, which you must. Second, it was validated on a snapshot of models and one engine, so treat the exact percentages as lab-conditioned, not guarantees. The direction is trustworthy; the numbers are not promises.
Translate the research into practice, and GEO looks like disciplined content work:
Put evidence next to claims. A relevant statistic or a credible quotation adjacent to an assertion gives a model something concrete to cite.
Cite your sources inline. Linking authoritative references makes your content more quotable and more trustworthy to an extracting model.
Write self-contained passages. A paragraph that answers a question completely on its own is easier to lift than one that depends on the three around it.
Use precise entity language. Name things clearly (products, standards, methods) so a model can attribute a fact to the right entity, rather than leaning on pronouns.
Improve fluency and structure. Clear headings, short paragraphs, lists, and tables help extraction.
Never keyword-stuff. It hurt in the research and it reads badly to humans too.
Notice how much of this is simply good writing and good SEO. That overlap is the point: independent practitioners estimate GEO is roughly 70 to 80 percent repackaged SEO, and the genuinely new work is the evidence-and-extractability layer, not a separate discipline.
Since the two overlap so heavily, it is worth stating the difference plainly in one place. SEO optimizes for ranking in a list of links, where the goal is a position and a click. GEO optimizes for being cited inside a synthesized answer, where there may be no list to rank in and the goal is inclusion and attribution.
The foundations are shared almost entirely: quality content, clear structure, genuine authority, and crawlability matter for both. What GEO adds on top is a formatting-and-evidence layer: self-contained passages a model can lift, statistics and citations sitting next to the claims they support, and precise entity language so a model can attribute a fact to the right source. You still do SEO; GEO is the layer that makes your already-ranking content quotable by a machine.
Here is what the evidence layer looks like in practice. Weak (hard to cite): "Reorder points are important and you should calculate them carefully to avoid stockouts." It asserts without giving a model anything concrete to quote.
Strong (easy to cite): "A reorder point is average daily sales multiplied by lead time in days, plus safety stock. For a SKU selling 20 units a day with a 30-day lead time and a 100-unit buffer, the reorder point is 700 units." Now there is a self-contained definition, a formula, and a worked number, exactly the kind of quotable, verifiable passage the research found gets cited. The rewrite is not longer for its own sake; it is denser with facts a model can extract and attribute.
For an Amazon seller, GEO applies to two different surfaces, and it helps to keep them separate. Your off-Amazon content (a brand blog, guides, comparisons) is what ChatGPT, Perplexity, and Google's AI features can crawl and cite, so that is where classic GEO tactics apply directly. Your Amazon listings, by contrast, live inside Amazon and are read mostly by Amazon's own AI, Rufus, so the "GEO" that matters there is intent-rich, complete listing content rather than blog-style citations.
The practical split: publish genuinely useful, evidence-backed content off Amazon to earn AI citations and brand mentions, and make your listings exhaustive and intent-rich to be favored by Rufus. Both are the same instinct (be the clearest, most trustworthy source) applied to two different engines.
A single "AI answer" hides very different plumbing, and optimizing for one engine does not guarantee the others. ChatGPT's search leans heavily on Bing's index, so ranking well in Bing matters. Perplexity uses hybrid retrieval and rewards freshness and extractability. Google's AI Overviews run over Google's own index and correlate with (but are decoupling from) traditional rankings.
One large citation study found little overlap in the sources different engines cite for the same query, which means there is no single lever. The defensible strategy is to strengthen the fundamentals (authority, structure, evidence) that travel across all of them, rather than chasing one engine's quirks.
With no ranking to track, GEO is measured by presence in answers:
Share of voice: the percentage of relevant AI answers that mention your brand. Vendors often cite 15 percent or higher as strong, though that is a rough benchmark.
Citation and mention tracking across ChatGPT, Perplexity, Gemini, and others.
Referral traffic from AI sources, which you can filter in GA4 (chatgpt.com, perplexity.ai, and similar).
Tools like Profound, Peec, and Otterly sample AI answers to estimate these. Crucially, AI citations are non-deterministic (the same prompt can yield different sources) and unstable over time, so a single "we got cited" screenshot is anecdote, not a KPI. Track trends, not moments.
GEO is not a replacement for SEO or a magic new channel; it is a sharpening of content quality for a world where a model may read and quote you instead of a human clicking through. The highest-leverage moves (real evidence, clear structure, genuine authority, being mentioned across many independent sources) are the ones that also make your content better for people.